Master Thesis: LLM-based log processing and Anomaly Detection in Cloud Orchestartion Systems

Technology, Data & Digital · Data, AI & Analytics · Data Engineering · Machine Learning · Software Engineering

In short

This Master's thesis will investigate the effectiveness of Large Language Models (LLMs) for log processing and anomaly detection in large-scale cloud orchestration systems, comparing them to traditional log parsing and ML techniques. The work will involve studying research, establishing baselines, designing and implementing both LLM-based and conventional pipelines, and evaluating their performance in detecting anomalies and diagnosing faults.

Responsibilities

Study relevant research on log parsing, anomaly detection, fault diagnosis, and LLM-based analysis of operational data.
Establish a representative baseline for healthy operation by collecting and analyzing logs from the target system under normal conditions.
Design and implement one or more conventional log-processing pipelines using log parsing and ML techniques.
Design and implement an LLM-based approach for log processing and anomaly detection.
Define, construct, and inject representative fault scenarios into the target environment.
Evaluate how well the different approaches detect anomalies, identify fault types, and determine probable root causes.
Analyze the results and assess the practical applicability of LLMs for monitoring large-scale distributed systems.

Requirements

Ongoing Msc studies within Computer Science, Electrical Engineering, Physics or Maths
Knowledge of LLMs, AI, ML, Cloud

Skills

Artificial IntelligenceAnalysisAlgorithmsAPIEngineeringComputer ScienceAuthenticationAnalyticsAmazon Web Services (AWS)AgileLLMAIMLCloud

#LLM#anomaly detection#cloud orchestration#log processing#machine learning#distributed systems#Kubernetes#OpenStack#thesis#master thesis

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